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Suzuka Yoshimoto

Publications and source records attributed to Suzuka Yoshimoto.

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Is Self-Admitted Technical Debt Tested? An Empirical Study of Coverage, Co-change, and Impact

When developers write a TODO or FIXME comment, they are explicitly admitting that the code is suboptimal: a built-in warning that this logic deserves extra scrutiny. Yet it is an open question whether Self-Admitted Technical Debt (SATD) actually receives that scrutiny in the form of software testing. We aim to characterize the relationship between SATD and testing across three dimensions: the extent to which SATD-affected code is covered by existing tests, whether developers synchronize test additions with debt resolution, and whether such testing affects the long-term observability of resulting defects. For that, we conducted an empirical study on eight open-source Java projects, analyzing test coverage of 784 SATD instances identified in the latest releases and performing a longitudinal examination of 5,175 SATD removal events. Our results show that while 60.7% of SATD-affected code is covered by existing test suites, developers rarely synchronize test modifications with debt resolution; manual inspection confirms that only 3.4% of SATD removal commits include new tests specifically targeting the resolved debt (vs. 12.5% that co-add tests in the same commit). Longitudinal analysis further suggests that SATD resolutions exhibit nearly identical localized bug induction rates within short-to-medium-term windows regardless of test modifications. However, over a longer, unrestricted observation window, a slight divergence emerges where the test-added group reaches a higher cumulative defect alignment probability (6.32% vs. 4.37%), a counterintuitive trend potentially driven by the selective testing of inherently complex components. Developers treat SATD repayment as an ordinary code change rather than as a high-risk maintenance activity: most debt removals proceed without targeted verification, despite the developer's own prior flag that the code is suboptimal.

cs.SE

Testing with AI Agents: An Empirical Study of Test Generation Frequency, Quality, and Coverage

Agent-based coding tools have transformed software development practices. Unlike prompt-based approaches that require developers to manually integrate generated code, these agent-based tools autonomously interact with repositories to create, modify, and execute code, including test generation. While many developers have adopted agent-based coding tools, little is known about how these tools generate tests in real-world development scenarios or how AI-generated tests compare to human-written ones. This study presents an empirical analysis of test generation by agent-based coding tools using the AIDev dataset. We extracted 2,232 commits containing test-related changes and investigated three aspects: the frequency of test additions, the structural characteristics of the generated tests, and their impact on code coverage. Our findings reveal that (i) AI authored 16.4% of all commits adding tests in real-world repositories, (ii) AI-generated test methods exhibit distinct structural patterns, featuring longer code and a higher density of assertions while maintaining lower cyclomatic complexity through linear logic, and (iii) AI-generated tests contribute to code coverage comparable to human-written tests, frequently achieving positive coverage gains across several projects.

cs.SE